PDF(Pubmed)
Abstract：
An ever-increasing amount of data on a person\'s daily functioning is being collected, which holds information to revolutionize person-centered healthcare. However, the full potential of data on daily functioning cannot yet be exploited as it is mostly stored in an unstructured and inaccessible manner. The integration of these data, and thereby expedited knowledge discovery, is possible by the introduction of functionomics as a complementary \'omics\' initiative, embracing the advances in data science. Functionomics is the study of high-throughput data on a person\'s daily functioning, that can be operationalized with the International Classification of Functioning, Disability and Health (ICF).A prerequisite for making functionomics operational are the FAIR (Findable, Accessible, Interoperable, and Reusable) principles. This paper illustrates a step by step application of the FAIR principles for making functionomics data machine readable and accessible, under strictly certified conditions, in a practical example. Establishing more FAIR functionomics data repositories, analyzed using a federated data infrastructure, enables new knowledge generation to improve health and person-centered healthcare. Together, as one allied health and healthcare research community, we need to consider to take up the here proposed methods.
摘要：
正在收集越来越多的关于一个人的日常功能的数据，它拥有信息来彻底改变以人为本的医疗保健。然而,关于日常运作的数据的全部潜力还不能被利用，因为它大多以非结构化和不可访问的方式存储。这些数据的整合，从而加快了知识发现，通过引入功能组学作为补充“组学”倡议是可能的，拥抱数据科学的进步。功能组学是研究一个人的日常功能的高通量数据，可以通过国际功能分类来实施，残疾与健康（ICF）。使功能组学具有可操作性的先决条件是FAIR(Findable，可访问,互操作,和可重用)原则。本文说明了FAIR原理的逐步应用，使功能组学数据机器可读和可访问，在严格认证的条件下，在一个实际的例子中。建立更多的FAIR功能组学数据存储库，使用联合数据基础设施进行分析，使新一代知识能够改善健康和以人为中心的医疗保健。一起,作为一个联合健康和医疗保健研究社区，我们需要考虑采用这里提出的方法。
